Staff AI Scientist

The SpringCube team curated the following job opportunity to help you in your job search. Explore the position below to find your next career move.

Company Overview

A pioneering AI technology company is focused on building trust into artificial intelligence by enabling organizations to deploy transparent, secure, and reliable AI solutions. Its platform helps AI engineers, data scientists, and business teams monitor, evaluate, secure, analyze, and improve AI applications and understand the factors behind AI outcomes.

Key Responsibilities

• Lead applied research and development for models and datasets supporting AI trust services, guardrail classifiers, and evaluators.
• Partner closely with engineering, product, and customer success teams to support enterprise customers and deliver measurable value from AI observability solutions.
• Build relationships with customer data science and machine learning engineering teams to understand their AI observability requirements.
• Design, train, and deploy production classifiers for safety, security, and quality detection, including prompt injection, jailbreaks, PII, hallucination, and faithfulness.
• Develop production-ready models while operating within strict latency and cost constraints.
• Lead synthetic and adversarial dataset development pipelines, including methods for generating, filtering, and validating data that exposes model failure modes.
• Drive the technical direction of generative insights, including LLM- and agent-powered analysis capabilities that help customers identify and diagnose problems in their AI applications.
• Contribute to evaluation and experimentation infrastructure for measuring model quality, regression, and drift across rapidly evolving model populations.
• Explore reinforcement learning and preference-based approaches when they provide advantages over supervised learning methods.
• Collaborate with backend and platform engineers to transform research prototypes into scalable, secure, observable production services.
• Partner with Product, Solutions Engineering, and Customer Success teams to translate enterprise customer needs into research initiatives and product capabilities.
• Mentor AI Scientists and raise technical standards through code reviews, design reviews, and technical guidance.
• Represent the organization through research publications, technical presentations, conferences, or open-source contributions when appropriate.

Required Qualifications

• 7+ years of applied AI experience with a demonstrated track record of taking machine learning models from research through production.
• Experience with LLM evaluations, agentic evaluations, and AI guardrailing.
• Deep expertise in training and fine-tuning classifier models, including modern encoder architectures such as BERT-family models, ModernBERT, and LLM-as-classifier approaches.
• Strong understanding of the tradeoffs between different classifier architectures and approaches.
• Hands-on experience with dataset development, including data sourcing, labeling, synthetic data generation, adversarial augmentation, and quality control.
• Strong applied experience with LLMs and agentic systems, including prompting, fine-tuning, and evaluation.
• Proficiency in Python and modern machine learning technologies such as PyTorch, Hugging Face, and common training and serving frameworks.
• Experience working in production environments and collaborating with backend and platform engineers on real-time inference, monitoring, and model rollout.
• Interest in using AI coding tools to increase engineering productivity while maintaining high standards for quality, security, and reliability.
• Excellent written and verbal communication skills, with the ability to explain research concepts and technical tradeoffs to engineers, product managers, and customers.
• Ability to work from the Palo Alto office 2–3 days per week.

Preferred Qualifications

• M.S. or Ph.D. in Computer Science, Machine Learning, Statistics, Physics, or a related quantitative discipline.
• Published research in leading machine learning or natural language processing conferences, such as NeurIPS, ICML, ICLR, ACL, or EMNLP.
• Experience with reinforcement learning, RLHF, RLAIF, or preference-based fine-tuning.
• Experience developing synthetic data generation pipelines at scale.
• Background in AI safety, red-teaming, or adversarial machine learning.
• Experience working with enterprise customers in regulated industries such as finance, healthcare, or government.

Disclaimer

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